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首页> 外文会议>International conference on human-computer interaction;International conference on digital human modeling >ECG Identification Based on PCA-RPROP
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ECG Identification Based on PCA-RPROP

机译：基于PCA-RPROP的ECG识别

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摘要

With the quick development of information technology, people pay more and more attention to information security and property safety, where identity is one of the most important aspects of information security. Compared with the traditional means of identification, biometrics recognition technology offers greater security and convenience. Among which, electrocardiogram (ECG) human identification has been attracted great attention in recent years. As a new type of biometric feature authentication technology, the feature selection and classification of ECG has become a focus of the research community. However, there exist some problems that can impair the efficiency and accuracy of ECG identification, including information redundancy and high dimensionality in feature extraction, and insufficient stability in classification. In order to solve the problems, in this paper, we propose a recognition method based on PCA-RPROP. In this method, firstly, only R points are located to get the original single-cycle waveforms. Then, PCA and whitening are used to process original data, where whitening is to make the input less redundant and PCA is to reduce its dimensionality. Finally, the resilient propagation (RPROP) algorithm is used to optimize the neural network and establish a complete recognition model. In order to evaluate the effectiveness of the algorithm, we compared the PCA feature with the wavelet decomposition and multi-point localization features in an ECG-ID database, and also compared RPROP with traditional BP algorithm, SVM and KNN. The experimental results show that this method can improve the performance compared with other classifiers, and simultaneously reduce the complexity of localization and the redundancy of features. It is superior to the other methods both speed and accuracy in recognition, especially when compared with the traditional BP. It can solve the problems of traditional BP with 2.4% higher recognition accuracy than LIBSVM, and 14 s faster than KNN in terms of time efficiency. Therefore, it is an efficient, simple and practical recognition algorithm.
机译：随着信息技术的飞速发展，人们越来越重视信息安全和财产安全，身份是信息安全最重要的方面之一。与传统的识别方式相比，生物识别技术提供了更高的安全性和便利性。其中，近年来，心电图（ECG）人的身份识别备受关注。作为一种新型的生物特征识别技术，心电图的特征选择和分类已成为研究界关注的焦点。但是，存在一些可能会损害ECG识别效率和准确性的问题，包括信息冗余和特征提取中的高维性以及分类中的稳定性不足。为了解决这些问题，本文提出了一种基于PCA-RPROP的识别方法。在这种方法中，首先，仅定位R个点以获得原始的单周期波形。然后，使用PCA和白化处理原始数据，其中白化将减少输入的冗余，而PCA则将减少其维数。最后，采用弹性传播（RPROP）算法优化神经网络并建立完整的识别模型。为了评估该算法的有效性，我们将PCA特征与ECG-ID数据库中的小波分解和多点定位特征进行了比较，还将RPROP与传统的BP算法，SVM和KNN进行了比较。实验结果表明，与其他分类器相比，该方法具有更好的性能，同时降低了定位的复杂度和特征的冗余度。它的识别速度和准确性均优于其他方法，尤其是与传统BP相比。它可以解决传统BP的问题，其识别精度比LIBSVM高2.4％，在时间效率方面比KNN快14 s。因此，它是一种高效，简单，实用的识别算法。

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